Mendelian randomization analyses of genetically predicted circulating levels of cytokines with risk of breast cancer
Bibliographic record
Abstract
Abstract To determine whether genetically predicted circulating levels of cytokines are associated with risk of overall breast cancer (BC), estrogen receptor (ER)-positive and ER-negative BC, we conducted two-sample MR analyses using data from the most comprehensive genome-wide association studies (GWAS) on cytokines in 8293 Finnish participants and the largest BC GWAS from the Breast Cancer Association Consortium (BCAC) with totally 122,977 BC cases and 105,974 healthy controls. We systematically screened 41 cytokines (of which 24 cytokines have available instruments) and identified that genetically predicted circulating levels (1-SD increase) of MCP1 (OR: 1.08; 95% CIs: 1.03–1.12; P value: 3.55 × 10−4), MIP1b (OR: 1.02; 95% CIs: 1.01–1.04; P value: 2.70 × 10−3) and IL13 (OR: 1.06; 95% CIs: 1.03–1.10; P value: 3.33 × 10−4) were significantly associated with increased risk of overall BC, as well as ER-positive BC. In addition, higher levels of MIP1b and IL13 were also significantly associated with increased risk of ER-negative BC. These findings suggest the crucial role of cytokines in BC carcinogenesis and potential of targeting specific inflammatory cytokines for BC prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".